Update model card for 3.9.6

07f7f72 verified about 2 months ago

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tags:
  - spacy
  - token-classification
language:
  - la
license: mit
model-index:
  - name: la_core_web_trf
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.9102
          - name: NER Recall
            type: recall
            value: 0.9126
          - name: NER F Score
            type: f_score
            value: 0.9114
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.9392
      - task:
          name: POS
          type: token-classification
        metrics:
          - name: POS (UPOS) Accuracy
            type: accuracy
            value: 0.9734
      - task:
          name: MORPH
          type: token-classification
        metrics:
          - name: Morph (UFeats) Accuracy
            type: accuracy
            value: 0.9395
      - task:
          name: LEMMA
          type: token-classification
        metrics:
          - name: Lemma Accuracy
            type: accuracy
            value: 0.9463
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0.8691
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.8204
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0.93

la_core_web_trf: Latin NLP pipeline for spaCy. Trained on harmonized Universal Dependencies treebanks (Perseus, PROIEL, ITTB, LLCT, UDante, CIRCSE) with LASLA data. Includes POS tagging, morphological analysis, lemmatization, dependency parsing, named entity recognition, and sentence segmentation.

_{« Catus » — from "De cato seu fele" in Conrad Gesner's Historiae animalium liber primus de quadrupedibus viviparis (1551), p. 318}

Feature	Description
Name	`la_core_web_trf`
Version	`3.9.6`
spaCy	`>=3.8.3,<3.9.0`
Default Pipeline	`enclitic_splitter`, `transformer`, `senter`, `token_fix`, `normer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `lookup_lemmatizer`, `uv_normalizer`, `parser`, `harmonizer`, `remorpher`, `ner`, `trf_vectors`
Components	`enclitic_splitter`, `transformer`, `senter`, `token_fix`, `normer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `lookup_lemmatizer`, `uv_normalizer`, `parser`, `harmonizer`, `remorpher`, `ner`, `trf_vectors`
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	CIRCSE/LASLA: LASLA Corpus LatinCy NER UD_Latin-CIRCSE UD_Latin-ITTB UD_Latin-LLCT UD_Latin-Perseus UD_Latin-PROIEL UD_Latin-UDante
License	`MIT`
Author	Patrick J. Burns
Contributors	Tim Geelhaar (annotation, error analysis [v3.5.2: morphologizer, tagger, parser]); Nora Bernhardt (NER); Vincent Koch (NER)

What's new in 3.9.6

This is a maintenance release across the LatinCy pipeline family (la_core_web_sm/md/lg/trf). Model weights are unchanged from v3.9.x — no retraining; the changes are to sentence segmentation and morphological post-processing behavior.

Fixed sentence segmentation at a terminal followed by a closing single quote. A closing single quote (' or ’) glued directly after a sentence terminal (., ?, !, ;) was suppressing a boundary the senter places for the closing double quote — so …non potestis?' Moritur… was left as a single sentence. The token_fix corrector now restores the missing boundary, bringing '/’ to parity with "; direct speech ending in a terminal + closing quote is again split from the following clause.
Added a deponent-verb tagging fallback. When the statistical tagger labels a known deponent lemma (e.g. sequor, loquor, hortor) as a plain verb, the harmonizer now corrects the fine-grained tag to verb/deponent, so deponents are tagged consistently even when the model misses the subcategory.